Classification of steel samples by laser-induced breakdown spectroscopy and random forest

Zhang, Tianlong; Xia, Donghui; Tang, Hongsheng; Yang, Xiaofeng; Li, Hua

doi:10.1016/j.chemolab.2016.07.001

Cited by 52 publications

(17 citation statements)

References 26 publications

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“…(Classification is performed by computing a quadratic function of observed discrete spectral components.) This highly contrasts with sophisticated and complex classifiers previously attempted in the literature [7,[9][10][11]42].…”

Section: Applicability Of the Optimal Classifiermentioning

confidence: 86%

“…Classification of LIBS data has been an active area of research. Automatic classification has been attempted on a variety of domains including mineralogy (classification of sedimentary ores [40], quartz samples [41], material science [42], botany [43], homeland security [44], and planetology [45]) The optimal classifier presented in the paper is relatively simple. (Classification is performed by computing a quadratic function of observed discrete spectral components.)…”

Section: Applicability Of the Optimal Classifiermentioning

confidence: 99%

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Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Pokrajac

Sivakumar

Markushin

et al. 2019

Int J Data Sci Anal

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Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of "dark signal" and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.

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Section: Applicability Of the Optimal Classifiermentioning

confidence: 86%

Section: Applicability Of the Optimal Classifiermentioning

confidence: 99%

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Pokrajac

Sivakumar

Markushin

et al. 2019

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…Classification, 98 especially supervised classification, identifies category membership assuming two or more classes, i.e., binary classification or multiclass classification. Many algorithms have been applied to the problem, e.g., linear discriminant analysis (LDA), 99,100 kernel approximation, 101 k-nearest neighbors, 102 naive Bayes, 103 support vector machine (SVM), [104][105][106][107] random forest (RF), 108,109 neural network (NN), [110][111][112][113] deep learning (DL), 12,114,115 etc. These algorithms are mainly enclosed in the scikit-learn.…”

Section: Classificationmentioning

confidence: 99%

Chemometrics and Related Fields in Python

Morita

2019

ANAL. SCI.

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The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

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“…Sheng et al used SVM and RF methods to discriminate and classify 10 types of iron ore samples; although both the SVM and RF models provided acceptably accurate predictions, RF provided better classification predictions. At the same time, Zhang et al proposed LIBS integrated with RF to identify and discriminate 9 steel grades, and its generation ability was evaluated by the out‐of‐bag estimation and 5‐fold CV. Compared with PLS‐DA and SVM, the RF model showed a better classification performance for steel samples.…”

Section: Qualitative Analysismentioning

confidence: 99%

Chemometrics in laser‐induced breakdown spectroscopy

Zhang

Tang

2018

Journal of Chemometrics

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Laser-induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of real-time, online, and noncontact as well as enabling the simultaneous analysis of multiple elements. It has become a frontier analytical technique in spectral analysis. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by extracting useful information from a large amount of complex LIBS data remains the main problem for the LIBS technique. Chemometrics is a chemical subdiscipline of multi-interdisciplinary methods; it offers advantages in data processing, signal analysis, and pattern recognition. It can solve some complicated problems that are difficult for traditional chemical methods. In this paper, we reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012-2016). KEYWORDSchemometrics, data preprocessing, laser-induced breakdown spectroscopy, qualitative analysis, quantitative analysis | INTRODUCTIONAs one of the most promising analytical tools in the 21st century, laser-induced breakdown spectroscopy (LIBS) is a novel analytical technique for materials and elements; it is based on atomic emission spectroscopy and a laser as an excitation source. 1-4 In LIBS, a low-energy pulse laser (typically tens to hundreds of millijoules per pulse) generated from a laser device is reflected by a plane mirror and focused by a plano-convex lens onto the sample surface; a laser-induced plasma with high temperature is then generated. The light with different frequencies is radiated during the plasma cooling process, and it is collected by an optical fiber coupled to a spectrometer. The qualitative and quantitative analyses by the LIBS technique can be performed using the wavelength and integrated intensity of the spectral line. A schematic diagram of the LIBS instrument is presented in Figure 1. It consists of Nd:YAG laser, optical part, X-Y-Z manual micrometric stage, optical fiber, spectrometer, Charge coupled device (CCD) camera, and computer.The LIBS technology has benefited from the invention of the ruby crystal laser 5 and was first reported by Brech and Cross. 6 It has become a research focus in the spectral field with the development of laser and optical detection techniques. Compared with conventional analytical tools, LIBS demonstrates some substantial advantages with regards to being rapid, real-time, on-site, and nondestructive while allowing remote detection without complicated sample preparation procedures as well as simultaneous multicomponent analysis. 7-9 Because of these advantages, LIBS technology has been widely used in process analysis and in the control of industrial manufacturing, food supervision, environmental monitoring, historical relic identification, and space exploration. [10][11][12][13] The National Aeronautics and

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Classification of steel samples by laser-induced breakdown spectroscopy and random forest

Cited by 52 publications

References 26 publications

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Chemometrics and Related Fields in Python

Chemometrics in laser‐induced breakdown spectroscopy

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